Data Engineering & Analytics Platforms
Data pipelines, lakehouse foundations, analytics layers and quality controls for teams that need trusted operational data.
Reference architecture
Cloud-Native Data Platform Architecture
A reference architecture for organising fragmented data flows into a portable, observable platform with explicit ownership and quality controls.
Architecture context
- Operational data may move through scripts, manual exports and team-owned tools.
- Analytics consumers need a defined path from source systems to governed reporting layers.
- Future automation and AI use depends on explicit data quality and lineage foundations.
Design constraints
- Existing systems continue operating while data flows are migrated incrementally.
- Platform components remain portable across private and hybrid environments.
- Operational ownership is defined before production adoption.
Reference architecture
- Staged ingestion separates raw, curated and serving data zones.
- Containerised data jobs use orchestration, retries and operational observability.
- Data quality checks and lineage documentation sit within transformation workflows.
- Access boundaries define responsibilities across ingestion, transformation and consumption.
Implementation considerations
- Prioritise the highest-risk data flows before expanding platform coverage.
- Define contracts and tests for reusable ingestion and transformation patterns.
- Prepare support, recovery and change-management runbooks alongside implementation.
Expected operational characteristics
- Data-product ownership is explicit across ingestion, transformation and serving layers.
- Pipeline health, quality failures and recovery states are observable.
- Platform patterns support incremental adoption rather than a single cutover.
Related services
Cloud-Native Infrastructure
Container, Kubernetes, GitOps and observability platforms for private, on-premises, hybrid and public cloud environments.